



'''simple docstring'''




import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


lowerCamelCase				:int							       =   1_6
lowerCamelCase				:int							       =   3_2



def       a     (					lowerCamelCase__		, lowerCamelCase__ = 16		, lowerCamelCase__ = "bert-base-cased"       ):
			'''simple docstring'''





			A_      :		List[str]      =	AutoTokenizer.from_pretrained(a_       )
			A_      :		Union[str, Any]      =	load_dataset("""glue"""		, """mrpc"""       )

			def tokenize_function(lowerCamelCase__       ):
						# max_length=None => use the model max length (it's actually the default)
						A_      :		List[Any]      =	tokenizer(examples["""sentence1"""]		, examples["""sentence2"""]		, truncation=a_		, max_length=a_       )
						return outputs

			# Apply the method we just defined to all the examples in all the splits of the dataset
			A_      :		Tuple      =	datasets.map(
			    a_		, batched=a_		, remove_columns=["""idx""", """sentence1""", """sentence2"""]		, load_from_cache_file=a_       )

			# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
			# transformers library
			A_      :		List[Any]      =	tokenized_datasets.rename_column("""label"""		, """labels"""       )

			def collate_fn(lowerCamelCase__       ):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						if accelerator.distributed_type == DistributedType.TPU:
									return tokenizer.pad(a_		, padding="""max_length"""		, max_length=1_28		, return_tensors="""pt"""       )
						return tokenizer.pad(a_		, padding="""longest"""		, return_tensors="""pt"""       )

			# Instantiate dataloaders.
			A_      :		Optional[int]      =	DataLoader(
			    tokenized_datasets["""train"""]		, shuffle=a_		, collate_fn=a_		, batch_size=a_       )
			A_      :		Dict      =	DataLoader(
			    tokenized_datasets["""validation"""]		, shuffle=a_		, collate_fn=a_		, batch_size=a_       )

			return train_dataloader, eval_dataloader



def       a     (					lowerCamelCase__		, lowerCamelCase__       ):
			'''simple docstring'''





			A_      :		int      =	Accelerator()

			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			A_      :		Tuple      =	config["lr"]
			A_      :		Tuple      =	int(config["""num_epochs"""]       )
			A_      :		Dict      =	int(config["""seed"""]       )
			A_      :		str      =	int(config["""batch_size"""]       )
			A_      :		Optional[Any]      =	args.model_name_or_path

			set_seed(a_       )
			A_      :		List[str]      =	get_dataloaders(a_		, a_		, a_       )

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			A_      :		Optional[Any]      =	AutoModelForSequenceClassification.from_pretrained(a_		, return_dict=a_       )

			# Instantiate optimizer
			A_      :		Dict      =	(
			    AdamW
			    if accelerator.state.deepspeed_plugin is None
			    or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
			    else DummyOptim
			)
			A_      :		List[Any]      =	optimizer_cls(params=model.parameters()		, lr=a_       )

			if accelerator.state.deepspeed_plugin is not None:
						A_      :		List[Any]      =	accelerator.state.deepspeed_plugin.deepspeed_config[
						    "gradient_accumulation_steps"
						]
			else:
						A_      :		List[Any]      =	1
			A_      :		str      =	(len(a_       ) * num_epochs) // gradient_accumulation_steps

			# Instantiate scheduler
			if (
			    accelerator.state.deepspeed_plugin is None
			    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
			):
						A_      :		Dict      =	get_linear_schedule_with_warmup(
						    optimizer=a_		, num_warmup_steps=0		, num_training_steps=a_		, )
			else:
						A_      :		Union[str, Any]      =	DummyScheduler(a_		, total_num_steps=a_		, warmup_num_steps=0       )

			# Prepare everything
			# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
			# prepare method.
			A_      :		Optional[Any]      =	accelerator.prepare(
			    a_		, a_		, a_		, a_		, a_       )

			# We need to keep track of how many total steps we have iterated over
			A_      :		Optional[int]      =	0
			# We also need to keep track of the stating epoch so files are named properly
			A_      :		List[Any]      =	0

			# Now we train the model
			A_      :		Union[str, Any]      =	evaluate.load("""glue"""		, """mrpc"""       )
			A_      :		List[Any]      =	0
			A_      :		Any      =	{}
			for epoch in range(a_		, a_       ):
						model.train()
						for step, batch in enumerate(a_       ):
									A_      :		Tuple      =	model(**a_       )
									A_      :		List[str]      =	outputs.loss
									A_      :		Tuple      =	loss / gradient_accumulation_steps
									accelerator.backward(a_       )
									if step % gradient_accumulation_steps == 0:
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

									overall_step += 1

						model.eval()
						A_      :		Optional[int]      =	0
						for step, batch in enumerate(a_       ):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									batch.to(accelerator.device       )
									with torch.no_grad():
												A_      :		str      =	model(**a_       )
									A_      :		Union[str, Any]      =	outputs.logits.argmax(dim=-1       )
									# It is slightly faster to call this once, than multiple times
									A_      :		Optional[Any]      =	accelerator.gather(
									    (predictions, batch["""labels"""])       )  # If we are in a multiprocess environment, the last batch has duplicates
									if accelerator.use_distributed:
												if step == len(a_       ) - 1:
															A_      :		Optional[int]      =	predictions[: len(eval_dataloader.dataset       ) - samples_seen]
															A_      :		Optional[int]      =	references[: len(eval_dataloader.dataset       ) - samples_seen]
												else:
															samples_seen += references.shape[0]
									metric.add_batch(
									    predictions=a_		, references=a_		, )

						A_      :		Optional[int]      =	metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(f'epoch {epoch}:'		, a_       )
						A_      :		int      =	eval_metric["accuracy"]

						if best_performance < eval_metric["accuracy"]:
									A_      :		Optional[Any]      =	eval_metric["accuracy"]

			if args.performance_lower_bound is not None:
						assert (
						    args.performance_lower_bound <= best_performance
						), f'Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'

			accelerator.wait_for_everyone()
			if accelerator.is_main_process:
						with open(os.path.join(args.output_dir		, """all_results.json"""       )		, """w"""       ) as f:
									json.dump(a_		, a_       )



def       a     (					):
			'''simple docstring'''





			A_      :		Dict      =	argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""       )
			parser.add_argument(
			    """--model_name_or_path"""		, type=a_		, default="""bert-base-cased"""		, help="""Path to pretrained model or model identifier from huggingface.co/models."""		, required=a_		, )
			parser.add_argument(
			    """--output_dir"""		, type=a_		, default="""."""		, help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""		, )
			parser.add_argument(
			    """--performance_lower_bound"""		, type=a_		, default=a_		, help="""Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value."""		, )
			parser.add_argument(
			    """--num_epochs"""		, type=a_		, default=3		, help="""Number of train epochs."""		, )
			A_      :		Union[str, Any]      =	parser.parse_args()
			A_      :		str      =	{"lr": 2E-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
			training_function(a_		, a_       )


if __name__ == "__main__":
						main()






'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCAmelCase									=   {
    """configuration_graphormer""": ["""GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GraphormerConfig"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase									=   [
					    """GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """GraphormerForGraphClassification""",
					    """GraphormerModel""",
					    """GraphormerPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_graphormer import (
										    GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    GraphormerForGraphClassification,
										    GraphormerModel,
										    GraphormerPreTrainedModel,
										)


else:
					import sys

					lowerCAmelCase									=   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



